How can we assess and evaluate the competitive advantage of a country’s human resource development system?
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The purpose of this study was to develop an index to assess and evaluate the competitive advantage of a country’s human resource development system. Based on an extensive literature review, a theoretical model of a human resource development system at the national level (named National Human Resource Development: NHRD) was constructed. The model consists of four factors—supply conditions, demand conditions, environment, and supporting systems—and NHRD system competitiveness was measured by the cumulative sum of a country’s achievement in each of these four factors. The four factors were divided into 10 sub-categories, and 45 indicators were selected based on criteria such as relevance, international comparability, reliability, timeliness, and accessibility. Finally, by using the Analytic Hierarchy Process, the individual weights of the 45 indicators were calculated. In addition, the relationships between the NHRD competitiveness scores of OECD member countries and other relevant variables, including GDP per capita and the scores from other national competitiveness studies, were analyzed to examine the validity of the index.
KeywordsNational human resource development National competitiveness Human resource competitiveness Human resource development policy Human resource development system
As the primary source of national wealth is shifting away from capital, labor, and technology to knowledge and human resources, the accumulation of intellectual and human capital and their supporting social systems and infrastructure are gaining more importance than ever in national development (Florida 2005; Garelli 2004; Kim 2005a; Michaels et al. 2001; Neef 1998; Pfeffer 1994). The fact that the development of human value and potential through continuous learning has become one of the most important national agendas, reflecting changes in society that have occurred over time (Kim 2005b, OECD 2001; Schuller 2010; Schuller and Brasset-Grundy 2002). Advanced countries have recently placed more emphasis on human resource development policies in their national development strategies. For example, the “Renewed Lisbon Strategy,” announced by the EU in 2005, set up as one of its 10 action plans greater investment in human capital through the promotion of education and vocational training. In addition, the “Innovate America” report, released by the US in 2004, adopted NHRD as one of its top three national innovation strategies and proposed three policy tasks to promote it. Also, in the case of the “Finland 2015: Balanced Development,” established by the Finnish government in 2001, five strategies out of 13 national agendas are either directly or indirectly related to NHRD.
New visions and strategies currently being implemented by advanced countries seem to imply that a country’s ability to develop human resources will become a touchstone of its future national development. Since a country’s future development potential largely depends on whether or not it has a well-established system that can effectively and efficiently acquire and cultivate human resources, the first thing to assess is how competitive a country’s human resource development system is in an international context. In this regard, developing a tool to assess and evaluate the competitiveness of a country’s human resource development system is essential for identifying the areas of strength and those in need of improvement.
There have been a few attempts, mainly in the US and Europe, to develop measures to assess human or intellectual capital at the corporation level (e.g., Edvinsson and Malone 1997; Flamholtz 1985; Sveiby 1997). Those indices have, however, some limitations for international comparison of the competitiveness of NHRD systems. Human or intellectual capital at the corporation level is mainly focused on training and development, and knowledge management.
Some assessment tools to measure human resource development at the national level have been developed. For example, the Global Talent Index (Kalman et al. 2011), developed in the UK, is one of such efforts to evaluate and internationally compare a country’s competitiveness in the area of human resource development. GTI has, however, some limitations. Selection rationale of seven sub-factors (Demographics, Compulsory education, University education, Quality of labor force, Talent environment, Openness, Proclivity for attracting talent) is not specified and, furthermore, why each indicator is chosen are not explained enough and number of indicators is not stable over time (47 in 2007, 30 in 2011).
The purpose of this study was to develop a tool to assess and evaluate the competitive advantage of a country’s HRD system. The following three research questions guided this study: (1) How can we define and conceptualize the NHRD system and its competitive advantage? (2) What are the components and indicators of the NHRD system? (3) How valid is the Global HRD competitiveness index, which was developed to evaluate NHRD competitiveness in this study?
Evolution of NHRD
Human resource development policy at the national level was implemented mostly in underdeveloped or developing countries of Asia and Africa which were impoverished after the Second World War and whose ultimate goal was to achieve modernization and economic and social growth (Kim 2002; Kim 2007). Those efforts were mainly focused on nurturing human resources through the expansion and reform of school education (Kim 2002, 2007; Lee and Kim 2009). Human capital theory and development economics, which emerged since the late 1950s, supported the belief that there is a causal relationship between education and economic growth. The seminal work was conducted by Harbison and Myers (1964), and they analyzed the correlation between human resource development and economic growth based on the data on 75 countries from 1958 to 1960. There was a high correlation of .89 between the human resource development composite index, consisting of primary and middle school enrollment rates and university enrollment rates, and the economic development index, measured by per capita GNP. They also believed NHRD to be a way of enhancing the knowledge, skills, and abilities of every single society member and explained NHRD from economic, political, and sociocultural perspectives.
The perspective known as the development education theory, or the education planning theory, was widely used as a foundational theory for national human resource development in underdeveloped and developing countries in their “Age of Development” in the 1950s and 1960s (Kim 2007). This perspective contributed to economic growth by cultivating human resources needed for industrial development (Adams 2002). Nevertheless, national efforts based on the development education theory are no longer sufficient in the new knowledge-based society because of their following limitations: First, since these endeavors only pursued the expansion of school education (namely, supply of human resources) without fully considering the labor market, there was no solution for academic inflation and unemployment of the educated. Second, by applying the development process of advanced countries as a one-size-fits-all approach, they failed to consider country-specific historical distinctiveness. Third, as a result of focusing too much on political stability and economic efficiency for industrialization, social class inequalities were further increased.
Discussion of NHRD in a knowledge-based economy is based on assumptions similar to those of the development education theory in that education is seen as a vital mechanism for national development, but differences from the past development education theory exist for the following reasons1: First, the advancement strategy of the NHRD covers the overall development of human ability, and it emphasizes not only economic development but also social and cultural development (Chae 2006; Jang et al. 2005; Kang and Kim 2005). On the other hand, the foundation of the development education theory was manpower planning which focused on the supply of human resources by estimating the manpower requirements of the specific industry or vocational field (Kim 2007).
Second, NHRD emphasizes the optimal level of matching between school education and the labor market (Jeong 2005; Kim 2005a). In this respect, NHRD takes into account not just the quantitative expansion but also the qualitative development of education. Third, NHRD is based upon the premise that the different and diverse systems of politics, economy, and education in each country affect the nature and role of NHRD, and customized development strategy is, therefore, required to meet a country’s specific situation (Cho and McLean 2004; Lynham and Cunningham 2006; McLean and McLean 2001). Fourth, NHRD has the perspective that the capacity and performance of human resources can be enhanced through mutual cooperation between the individual and organization as well as among organizations, and thus it places emphasis on social networking and social integration (Cho and Kim 2007; Kim 2005; OECD 2001).
Previous efforts to evaluate NHRD system competitiveness
Since Harbison-Myers' seminal index (1965) 2 was developed, the efforts to evaluate NHRD system competitiveness have been presented with different foci, interests, and subjects. Those existing indices can be largely divided into three types. The first type made an attempt to assess NHRD competitiveness directly even though different labels were utilized. These indices, such as the Talent Global Competitiveness Index and Global Talent Index, identified the elements of NHRD and attempted to measure the competitive advantage of human resources.
The second type is the partial measure of a human resource development system included among the measures of overall national competitiveness. These indices cover a wide spectrum of national indicators to assess each country’s competitive advantage, and some of the indicators are related to NHRD. The International Institute for Management Development (IMD)’s National Competitiveness Index, World Economic Forum (WEF)’s Global Competitiveness Index, and the Institute for Industrial Policy Studies (IPS)’s National Competitiveness Index are typical examples. Finally, the third type is a measure designed to assess such specific traits of human resource as creativity. What follows is a brief overview of those indices.
Talent global competitiveness index
The Talent Global Competitiveness Index (TGCI) was developed and published in China in 2010. With the premise that talented people are the sources of national competitiveness, it assesses Chinese competitiveness related to human resources (literally in this case, talented people) and compares it with other countries but also prospects for future challenges in acquiring talent (倪鹏飞, 潘晨光, 2010). The TGCI examines national talent competitiveness from a system perspective, specifically focusing on the two interacting factors of input and output in a national talent system. In addition, economic theories such as international trade theory, new growth theory, and human capital theory as well as other works on competitiveness—Prahalad and Hamel (1990)’s core competence model and Porter (1990)’s diamond model, to name a few—undergird the index.
The TGCI includes, as the input factors of a national talent system, Talent Entities and Talent Atmosphere. Talent Entities assess the structure of present and potential human resources with 39 indicators, and Talent Atmosphere assesses the environment for the people to live, start a business, and innovate using 48 indicators. TGCI also includes, as the output factors of a national talent system, Talent Wealth and Talent Innovation. Talent Wealth estimates the overall economic wealth produced using 10 indicators, and Talent Innovation estimates the output particularly related to innovation with nine indicators.
The TGCI is one of the few attempts to conceptualize national competitiveness in terms of human resources. However, there are some limitations as follows: First, by including indicators with low relevance or overlapped indicators among over 100 indicators, the validity and efficiency of an index that assesses and predicts human resources competitiveness are decreased. Second, by including indicators related to economic size (e.g., GDP and GDP per capita) in itself, the intention to measure the human resource competitiveness rather than the economic competitiveness of a country was not well achieved. Third, even though the matching between the supply of necessary human resources and the demand of labor markets is important in the system approach of input, process, and output, the relationship between education and the labor market was rarely considered.
Global talent index
The Global Talent Index (GTI) was developed in 2007 jointly by the Heidrick and Struggles International Inc. and the Economist Intelligence Unit (EIU), an economy analysis division under the Economist. The GTI evaluates whether a country has sufficient capacity for developing, attracting, and retaining talent by noting the following 7 conditions: Demographics (11.1 %), Compulsory education (11.1 %), University education (22.2 %), Quality of the labor force (22.2 %), Talent environment (11.1 %), Openness (11.1 %), and Proclivity for attracting talent (11.1 %). Each category was weighted based on experts’ opinions and research findings (the percentages in parentheses are those weights), and the index has 30 indicators in all (Kalman et al. 2011).
The strongest point of the GTI is that it forecasts national competitiveness over the following 5 years based on the analysis of the current conditions for developing, attracting, and retaining talent. However, it has shortcomings in that the theoretical backgrounds regarding the influences of the indicators on a country’s capacity for developing, attracting, and retaining talent have not been verified. Also, the great variability of indicators is another limitation (47 indicators were selected in 2007, but only 30 indicators remained in the report published in 2011).
The Latin America Talent Index (LATI), published one year later than the GTI, is an in-depth version of the original GTI focusing on the specific region, namely 10 countries from Latin America (Rebello 2008). While it is identical with the GTI in that it assesses the talent pool at the national level and uses the same methodology, LATI consists of slightly different indicators. The indicators of the LATI were selected based on the relevance in the regional context and the accessibility of the data. Thus, allowing comparison between the regional competitors, a direct comparison between the GTI and the LATI is meaningless. Also, the use of estimates for not a few indicators, by including a number of the data-poor economies, limits the accuracy of the LATI.
IMD national competitiveness index
The IMD has been evaluating the national competitiveness of major countries and publishing the World Competitiveness Yearbook every year since 1989. The national competitiveness index consists of four factors: Economics, Governmental efficiency, Efficiency of corporate management, and Infrastructure. Education is included as a sub-factor of Infrastructure and is measured by indicators such as total public expenditure on education, pupil–teacher ratio, higher education achievement, PISA score, and illiteracy. Related to NHRD, there are also other relevant indicators such as total R&D personnel nationwide, scientific articles, female positions, employee training, and brain drain.
One common criticism of the National Competitiveness Index pertains to the absence of theoretical backgrounds (Cho and Moon 2006). Furthermore, the weights of the indicators were determined rather subjectively and arbitrarily (hard indicators were given a value of 1, and soft indicators were given a value of .55). In addition, about one-third of the indicators are measured through entrepreneurs’ subjective ratings, which can possibly result in low reliability of the obtained national competitiveness scores.
WEF’s global competitiveness index
The WEF has been publishing the Global Competitiveness Report every year since 1996. The underlying model of global competitiveness, which is based on both Porter’s five-force model (1990) and Sala-i-Martin’s model (WEF 2010), consists of three categories: Basic requirements, Efficiency enhancers, and Innovation and Sophistication factors. While primary education is included as a pillar of the Basic requirements category, Labor market efficiency and Higher education are included as pillars of Efficiency enhancers. Those pillars include indicators such as the school enrollment rate, quality of the educational system, cooperation in laborer–employer relations, and female participation in the labor force, to name a few.
The Global Competitiveness Index (GCI), similarly to the IMD index, has been also criticized for its lack of theoretical backgrounds and its heavy use of subjective ratings (Cho and Moon 2006). In addition, as the selection of the indicators of competitiveness is based on the perspective of advanced countries, some researchers criticize the GCI for the unilinear and mechanical point of view underlying its research framework of economic development (Park 2006).
IPS national competitiveness index
The Institute for Industrial Policy Studies (IPS) and the Institute for Policy and Strategy on National Competitiveness (IPS-NaC), located in Korea, publish annually the IPS National Competitiveness Research Report. Based on the Double Diamond-based Nine-factor Model (Cho et al. 2008 2009), the IPS index evaluates national competitiveness using four physical factors of a country’s industry (Factor conditions, Demand conditions, Related industries, and Business context), four Human factors (Workers, Politicians and Bureaucrats, Entrepreneurs, and Professionals), and Chance events as a pure external environmental factor. The area of education, a sub-factor of Related industries, is measured by indicators such as public spending on education and students per teacher. Labor force, a sub-factor of Workers, is measured by indicators such as population, life expectancy at birth, and employment rate. Related to NHRD, there are also other relevant indicators such as professional’s education level, professional manager’s international experience, and number of professionals.
The IPS National Competitiveness Research Index has strengths, especially in that it has solid theoretical backgrounds. Also, it provides specific strategies for countries to develop their competitiveness. On the other hand, most of the indicators related to Human factors (e.g., those pertaining to Politicians, Entrepreneurs, and Professionals) were from survey questions answered by entrepreneurs only, thus limiting the reliability of the ratings.
The Centre for Cultural Policy Research of the University of Hong Kong and the Home Affairs Bureau of Hong Kong jointly developed the Creativity Index in 2005. The index consists of five categories, and 88 indicators selected based on the “5Cs Model of Creativity,” which presents the interaction between Human capital, Cultural capital, Social capital, Structural/Institutional capital, and Outcomes of creativity (Hui et al. 2005).
The Creativity Index has a unique theoretical model developed from existing researches on creativity such as Eysenck (1996) and Florida (2002). However, this index has a restriction on international application because all of its indicators are not internationally comparable. Although international comparison was not the main purpose of the study, this restriction is impeding further researches expanded to other countries. In addition, it lacks any follow-up studies after 2005 and, thus, has failed to provide research continuity.
Firstly, based on the review of previous literature related to national competitiveness and NHRD, NHRD system competitiveness was defined and the factors determining NHRD system competitiveness were identified. In this process, relevant theories—Human Capital Theory, Social Capital Theory, Labor Market Theories, and New Growth Theory (Endogenous Growth Theory), to name a few, were also explored. As a result, the conceptual model and the measurement model, which provided the basis of the index developed in this study, were established.
Secondly, the indicators of the factors and sub-factors included in the measurement model were selected referring to the European Quality Standard. The most important principle for selecting those indicators was relevance, which refers to a measure’s degree of scientifically proven relatedness to human resource development at the national level. In order to find more relevant indicators for the factors and sub-factors, findings from previous empirical studies were examined. Besides relevance, other principles such as reliability and international comparability were considered. Reliability refers to whether data on a measure can be collected through a reliable source. International comparability refers to whether data on a measure can be collected internationally.
Finally, the validity of the index was examined in three different but complementary ways. First of all, the models and the indicators have been revised and supplemented based on the comments of a panel of experts as to whether they are valid and have been properly selected. Ten experts from such diverse disciplines as business management (1), engineering (1), statistics (1), educational evaluation and measurement (1), higher education (1), educational psychology (3), educational technology (1), and educational philosophy (1) participated in the panel (numbers in parentheses indicate the number of experts who participated). Three to four experts were invited in one panel discussion (one and a half to 2 h) for the intensive exchange of ideas and feedback, and four rounds of panel discussions were conducted. The panel discussion method was utilized since, in this type of exploratory research, qualitative discussion and argument were considered more important than numerical consensus like the Delphi technique.
Second, the Analytical Hierarchy Process (AHP) was conducted with the expert group to decide the relative importance of each indicator. The AHP is a structured technique developed for analyzing complex decision situations. It was invented by Thomas L. Saaty in the 1970s and has been extensively utilized in a wide variety of group decision-making situations in fields such as education, business, government, industry, and healthcare (Bhushan and Rai 2004; Saaty 1995, 2001). AHP instruction developed by Saaty (2001) was used, and 17 experts from a variety of disciplines related to NHRD participated in the AHP in order to reflect on various perspectives regarding NHRD. Those experts are professors and researchers at universities or government-funded research institutes in Korea in the fields of human resource policy (2), labor economics (2), econometrics (1), technology management (1), public administration and policy (1), business administration (1), sociology (1), educational policy (2), higher education (1), lifelong education (1), international education (1), women’s studies (1), and statistics (2).
Third, in order to establish the criterion-related validity of the GHRD Index, a correlation analysis was conducted between the GHRD index and other indices such as the IMD National Competitiveness Index, the WEF Global Competitiveness Index, and the UNDP Human Development Index (HDI). These indices were selected as they are the most widely acknowledged and frequently used to compare the competitive advantage of nations. The correlation of the GHRD index with GDP per capita was also calculated to explore the degree to which NHRD is related with a country’s economic development.
Development of the global HRD competitiveness index
A conceptual and measurement model of NHRD
Definitions of NHRD
All such efforts at the national or societal level, such as education, training, cultural activity, and system improvement, in order to efficiently develop, produce, allocate, retain, and utilize human resources (p. 6)
McLean and McLean (2001)
Human resource development is any process or activity that, either initially or over the long term, has the potential to develop adults’ work-based knowledge, expertise, productivity, and satisfaction, whether for personal or group/team gain, or for the benefit of an organization, community, nation or, ultimately, the whole of humanity (p. 322)
Human Resource Development Act (2002)
Human resource development is defined as all the activities that central or local governments, education or research institutions, corporations or other institutions conduct for the purpose of developing, allocating, and utilizing human resources as well as building up HRD-related social norms and networks (article 2)
Jang et al. (2005)
The overall efforts by a nation or society to create, utilize, and diffuse knowledge through the development and efficient management of human capital and social capital for the purpose of personal development and national competitiveness enhancement (p. 10)
Lynham and Cunningham (2006)
HRD is a field of professional thought and practice operating to serve many different performance systems. when nations are the targeted performance system, the purpose of HRD is to develop and unleash human expertise for national economic performance, political and social development, growth, and well-being by enabling and enhancing the learning and performance capabilities of individuals, family units, communities, other social groupings, organizations (of all types), and thereby the nation as a whole (p. 119)
The definitions provided by McLean and McLean (2001) and Lynham and Cunningham (2006) include the nation as one of the main beneficiaries of human resource development. The definitions proposed by Korean scholars and the Human Resource Development Act encompass such human resource activities at the national level as the acquiring, developing, allocating, and utilizing of human resources. National human resource development is also closely related to knowledge creation and social capital development (Human Resource Development Act 2002; Jang et al. 2005).
In this study, NHRD was defined as a system that a country possesses in order to acquire, develop, and utilize its human resources since the critical areas of NHRD can be classified into acquiring, developing, and utilizing human resources, and those areas interact in a systemic way. Acquiring human resources, as the input of the NHRD system, means the efforts related to the accumulation and flow of human resources. Developing human resources, as the cultivating process of acquired human resources, means the process through which individuals build up their competencies through education, learning, experience, jobs, relations, etc. Utilizing human resources means activities related to creating economic or social value in labor markets as well as other areas of social activities.
In this study, the NHRD system was, therefore, assessed by the two main factors of supply and demand conditions and the two interrelated factors of supporting systems and environment. These four factors composed the components of the NHRD system and played the role of determining the competitive advantage of an NHRD system. Thus, NHRD competitiveness was measured by the levels of those four components, and the NHRD competitiveness score is, therefore, the cumulative sum of a country’s achievement in each of the four factors. Figure 3 illustrates the measurement model of NHRD.
Selection of the indicators
Based on the measurement model of NHRD depicted in Fig. 3, the following sub-factors and indicators were selected.
Supply conditions in the NHRD system pertain to the acquisition and development of human resources that can contribute to a country’s economic and social development. This study explores the supply conditions of the NHRD system from both quantitative and qualitative aspects because not only the number of human resources but also the quality of human resources affects a country’s development.
Quantitative aspects of supply conditions
Human reproduction is a basic necessary condition of NHRD since it is hardly expected that the required human resources in many occupational areas can be supplied without acquiring a certain population size. Previous empirical studies have shown a positive relationship between the size of human resources and national economies (Ilbo 2005; Kim and Woo 2009; Kitov 2006). Human capital theory, in particular, implied that the size of educated human resource is critical for economic prosperity because education reinforces individual productivity (Becker 1964; Denison 1962; Schultz 1960, 1961, 1963; Nelson and Phelps 1966).
Other indicators to measure the quantitative aspects of supply conditions include total fertility rate (Ilbo 2005; Kim and Woo 2009), life expectancy (Barro and Sala-I-Martin 1995; Croix and Licandro 1999), percentage of working-age population (Heller 2003; Higgins 1998), percentage of population with at least upper secondary education (Becker 1964; Denison 1962; Psacharopoulos 1984), percentage of population with tertiary education(Machin and McNally 2007), and percentage of population aged between 25 and 64 in adult learning (Schuller 2010; Schuller and Brasset-Grundy 2002).
Quality of supply conditions
As explored above, a sufficient number of well-educated human resources are required for a competitive NHRD system. However, this does not necessarily mean that an extended period of education leads to the development of human resources and drives economic growth. What is also critical is an individual’s ability to participate in the labor market and economic activities. Thus, in addition to the quantitative aspects of supply conditions, such quality components of supply conditions were included as the quality of education at different school levels (Fuller et al. 1986; Hanushek and Kimko 2000; Jamison et al. 2007) and health (Hildebrand and Van Kerm 2009; Jamison et al. 2005; Miguel 2005); since they are the principal factors, many scholars have considered as the key drivers of national growth.
To represent the quality of secondary education, the Program for International Student Assessment (PISA) score (Cheung and Chan 2008; Hanushek 2002) was selected. The number of universities on the list of world top 500 universities and the quality of management schools (MacMillan et al. 1986) were chosen in order to measure the quality of tertiary education. Finally, perceived health status was selected as an indicator as well (Miilunpalo et al. 1997).
When human resources are utilized properly, their competency will develop with work experience, and social and economic values are created in the labor market (McGuinness 2006). Demand conditions in the NHRD system are concerned with the utilization of human resources developed within or across the national educational system. Demand conditions also reflect both the quantitative and qualitative aspects of a human resource development system, like supply conditions.
Quantitative aspects of demand conditions
The quantitative aspects of demand conditions pertain to how many human resources are utilized in the labor market. The employment and unemployment rates are the representative indicators that show the utilization of human resources in a country (Bernstein and Baker 2003; Ewald 1999; Fagerberg et al. 1997; Sögner 2001). As utilizing high-quality human resources becomes more important in a knowledge-based society, the employment and unemployment rates of people with tertiary education degrees need to be emphasized. Female workforce participation in the labor market is also selected since female workers compose a more critical element of workforce competitiveness in a knowledge-based economy (Klasen 1999; Tzannatos 1999).
In conclusion, five indicators were selected to represent the quantitative aspects of demand conditions: the employment rate (Bernstein and Baker 2003; Ewald 1999), the unemployment rate (Fagerberg et al. 1997; Sögner 2001), the number of those 25–64 years old with tertiary education who are employed as a percentage of the total population of all people 25–64 years of age (Cho 2010; OECD 2010), the number of those 25–64 years old with tertiary education who are unemployed as a percentage of the total population of all people 25–64 years of age, and the rate of female labor force participation (Tzannatos 1999).
Quality of demand conditions
The quality of demand conditions means how effectively human resources are utilized in the labor market, more specifically, the structure and the attractiveness of the labor market. As the social and economic value that knowledge workers and experts produce in a society increases, labor market conditions inducing professionals and technicians appeared to be an important contributor of the economic and social growth of a nation (Nakamuro and Ogawa 2010; Florida 2002, 2005; Drucker 2001). In addition, various research studies have shown that gender equality affects economic growth via various routes (Klasen 1999).
The quality of demand conditions was, thus, measured by the following four indicators: the number of creative professionals as a percentage of the total population (Florida 2002; 2005), the number of technicians and associate professionals as a percentage of the total population, brain gain (Nakamuro and Ogawa 2010), and the gender gap in the median earnings of full-time employees (Klasen 1999).
Environment was defined as the sociocultural conditions and infrastructure that affect the human resource development of a country. Environment exists outside of the supply and demand conditions but affects them. Environment was composed of the following four sub-factors: technology, social capital, globalization, and industrial sophistication.
In this study, technology refers to technological infrastructure that affects the development and utilization of human resources in a country. Technological infrastructure promotes the generation, expansion, and use of knowledge and information as well as increases in personal and corporate productivity (Romer 1990; Warsh 2007). Since technology enhances the efficiency of work, human resources equipped with advanced technology bring about more high-quality products and services. Among various forms of technological infrastructure, information and communication technologies are particularly important when it comes to sharing and disseminating knowledge and information and thus affects human resource development. The economic effects of information and communication technologies infrastructure have been also empirically supported by many researchers (Oliner and Sichel 2003; Röller and Waverman 2001).
The following five indicators of technology were chosen: the number of fixed/mobile broadband Internet subscriptions as a percentage of the total population, international Internet bandwidth bit/s per Internet user, the number of estimated Internet users as a percentage of the total population, the number of mobile cellular telephone subscriptions as a percentage of the total population, and the ICT price.
Human resource development at a national level is influenced by social capital, which is defined as “features of social organization such as networks, norms, and social trust that facilitate coordination and cooperation for mutual benefit” (Putnam 1995, p. 67). Social capital is understood as a major factor directly affecting the performance of education (Beaulieu et al. 2001; Coleman 1988; Lee and Croninger 2001; Putnam 1995). Furthermore, the generation of social capital provides the rules of networking and reciprocity as well as facilitates cooperation and collective action (Segageldin and Grootaert 2000).
Four indicators mainly considered as representing social capital by many theorists and researchers were chosen for this study: interpersonal trust (Bourdieu 1986; Fukuyama 1995; Morrone et al. 2009), confidence in social institutions (Giddens 1990; Kilpatrick et al. 2001; Zucker 1986), tolerance (Florida 2002; OECD 2007; Wagner and Zick 1995), and voter turnout level (Edwards and Foley 2001; Guiso et al. 2004; Putnam 1995; Walzer 1995).
Globalization is a process in which political, economic, and sociocultural networks among individuals, organizations, and states are generated through the exchange of people, information, ideas, capital, and goods (Clark 2000; Norris 2000; Keohane and Nye 2000). Globalization has become an important environmental condition in which human resource development policies are designed and implemented as global economy and society play a decisive role in a country’s economic and social development (Marquardt 2007).
The level of a country’s globalization was measured with indicators representing the mobility of human resources and knowledge exchange that occur between and among countries (Navaretti and Tarr 2000; Peri 2005). In particular, the following five indicators were selected: the mean TOEFL score, foreign students as a percentage of all tertiary enrollments (Chellaraj et al. 2008), the number of international meetings per GDP (Rogers 2008), the number of international passengers as a percentage of the total population, and political globalization.
As a country’s industrial sophistication advances, more opportunities for developing and utilizing more specific and diverse fields of human resources are likely to be provided (Lynham and Cunningham 2006). For example, high-tech industry development requires the development of more specialized information technology majors in universities because it requires highly advanced knowledge and skills as well as employs more people than the manufacturing industry.
A country’s industrial sophistication is affected by the development or maturity of creative, knowledge or high-tech industry (Cho and Moon 2006; Florida 2002, 2005; Nakamuro and Ogawa 2010). The size of the exports and imports of a specific industry relative to that of the entire industrial sector was selected as a proxy measure. Thus, the development of the creative industry was measured by the size of the exports and imports of creative goods as a percentage of the total trades in goods; the development of the knowledge industry was measured by the receipts of royalties and license fees as a percentage of the total services exported; and the development of high-tech industry was measured by the size of hi-technology exports as a percentage of the total manufacturing exports.
The supporting systems of NHRD refer to a country’s investment and policy infrastructure that directly or indirectly support the acquisition, development, and utilization of human resources. Investment in human resource development activities from public and private resources, law or other supporting systems is critical elements to foster NHRD competitiveness (Keller 2006; Nelson and Phelps 1966; Schultz 1961). National innovation system theory implies that the development of a nation’s science and technology is facilitated by intensive interactions between public and private areas and supportive system networks (Carlsson and Jacobsson 1997; Lundvall 1992; Nelson and Rosenberg 1993).
Investment in education and research
Research based on human capital theory has shown that investment in education promotes economic growth (Barro 2002; Denison 1962; Harbison and Myers 1964; Psacharopoulos 1984; Schultz 1963). Investment in education has also positive impacts on such areas as health, life expectancy, citizenship, crime rates, and poverty rates (Appiah and McMachon 2002; Dee 2004; Kenkel 1991; Lochner and Moretti 2003), which, in turn, affects human resource development and national development. Furthermore, investment in research and development affects corporate productivity and performance and national economic growth (Chakrabarti and Lenard 1993; Nadiri, 1993). Four indicators of investment in education and research were selected as follows: public expenditure on education, private expenditure on education, government expenditure on R&D, and business expenditure on R&D.
A well-established policy infrastructure positively affects the development and utilization of human resources. For example, intellectual property protection influences economic growth by stimulating R&D investment in advanced countries (Gould and Gruben 1996; Park and Ginarte 1997). Affirmative policy for gender equality is also considered as one of the factors to advance not only national development but also economic growth (Klasen 1999; Tzannatos 1999). Thus, intellectual property protection and paid maternity leave are indicators of policy infrastructure to support an NHRD system. In addition, duration of compulsory education and public expenditure on affirmative labor market policies reflect national efforts to increase the quality of human resources and public policy to improve labor market conditions, respectively.
In conclusion, a total of 45 indicators which measure four factors—supply conditions, demand conditions, environment, and supporting systems—were selected to construct the GHRD index. Among the 45 indicators, 35 indicators (78 %) were based on hard data and 10 indicators (22 %) on soft data. The source of the data is provided in “Appendix 1.” Validation of this index was conducted as follows.
Validation of the global HRD competitiveness index
After constructing the conceptual and measurement model and selecting the indicators, an expert panel was composed, as previously mentioned in the Methods section of this paper, to validate the model, its components, and indicators. The expert panel members were asked to answer two open-ended questions about the validity of the NHRD model, components, and indicators. The first question was “Do you think that a conceptual and measurement model of human resource development in a country is validly constructed and why?” The second question was “Do the sub-areas of the four components and each indicator represent a country’s human resource development system properly?”
Critical comments from the expert panel are summarized as follows: First, human resource development is an open system which interacts with local and global environments. Second, both the qualitative and quantitative aspects of human resource are critical for social and national development. Third, a country’s human resource development is considerably influenced by labor market conditions. Fourth, the youth unemployment rate is an important indicator since it simultaneously reflects the quality of higher education and labor market conditions. Fifth, the school system of each country differs substantially, and an internationally comparable indicator to measure the quality of elementary and middle schools should be added. Sixth, workers in creative industry play a more critical role than ever in creating social and economic value in a knowledge-based society. Finally, lifelong learning and female workers’ participation in the labor market will influence human resource development.
Based on the experts’ comments, the measurement model was revised and some indicators were added or dropped. The initial measurement model had the following four factors: supply conditions, supportive systems, international collaboration and networks, and policy. The principal revised aspects of the model as follows. First, the NHRD measurement model, in response to the first comment, was revised to have characteristics of an open system (von Bertalanffy 1968). Specifically, environment was added as a factor either to reinforce or to restrain the supply and demand conditions.
Second, the demand condition of human resource development was included in the model since the interaction between the supply and demand conditions of NHRD is critical. Furthermore, both the quantitative and qualitative aspects of supply and demand conditions were reflected in the model in response to the second and third comments. Third, international collaboration and networks was added in the category of environment. Fourth, a policy component was included in the category of supporting systems. Thus, the four factors of NHRD, supply conditions, demand conditions, supporting systems and environment, were finally determined. Fifth, indicators to measure knowledge or creative industry, supportive systems for female workers’ participation in the labor market, and lifelong education were included.
Relative importance of the indicators
The impact size of the four factors of the measurement model and its indicators in NHRD system competitiveness were assumed to be different in this study. Previous numerous researches reviewed imply relative differences of the impact of each factor on the NHRD system (Adams 2002; Cho and Moon 2006; Garelli 2004; Kim 2005a; Lynham and Cunningham 2006; OECD 2001; Porter 1990; Reiljan et al. 2000). There have been various weighting methods utilized, from statistical models (e.g., factor analysis, regression analysis) to participatory methods (e.g., conjoint analysis, analytic hierarchy process) (OECD 2008). Among those methods, the Analytic Hierarchy Process (AHP) was adopted for this study based on the analysis of the strengths and weaknesses of those methods. First, statistical methods are hardly utilized in competitiveness index development due to the difficulties of identifying dependent variables. Second, the AHP is the most frequently utilized in research where social consensus needs to be built by participation (OECD 2008; Saaty 2001).
The first step of the AHP is, referring to the instruction of Saaty (2001), to decompose the decision problem into a hierarchy (three hierarchies in this study: four NHRD factors, 10 sub-factors, and 45 indicators), and then participants perform evaluations on each element’s relative importance based on their expertise and knowledge about the problem. Seventeen experts3 were given 96 comparison questions about the relative importance of each component in 3 hierarchies. Then, these evaluations were converted to numerical values resulting in numerical weights which signify each indicator’s relative ability to achieve the decision goal. Each individual participant’s consistent evaluation is critical so that the consistency rate is calculated in the AHP; 10 % and less than 20 % of the consistency rate is best suited and acceptable, respectively, and 20 % of the consistency rate was adopted in this study.
“Appendix 1” presents the weights given to 45 indicators of the Global HRD competitiveness index as a result of the AHP. The weights ranged from 9.14 to 0.25 % depending on the relative importance of the indicators. The two largest weights were given to the indicators such as number of creative professionals as a percentage of the total population (9.14 %) and the number of universities included in the list of the world top 500 universities (8.95 %).
Correlation with other indices
Correlation between global HRD competitiveness and other national competitiveness scores
As depicted in Table 2, the NHRD competitiveness score is highly correlated with the scores of HDI (r = .803, p < .001, the IMD scores (r = .816, p < .001, and the WEF scores (r = .872, p < .001). The analysis shows that a country that has a highly competitive NHRD system also shows high levels of national competitiveness (IMD, WEF) and human development (HDI).
The social development paradigm shift into a knowledge-based society in which knowledge and human resources are the two most salient components of social progress and economic development has attracted new attention to the importance of acquiring, developing, and utilizing human resources. This study was conducted to develop an assessment tool which can measure and compare how well each country acquires, develops, and utilizes human resources. A conceptual and measurement model of NHRD was constructed which assesses a country’s level of human resource development system. The three conceptual components of HRD at the national level were identified as acquiring, developing, and utilizing human resources, and then these components were measured with the four measurement factors of supply conditions, demand conditions, environment, and supporting systems. Ten sub-factors of 4 factors and 45 indicators were developed. This NHRD measurement model was validated through 4 rounds of expert panel discussions, and the AHP method was used to determine the weights for each indicator. Correlation analysis of the GHRD index score with the IMD and WEF national competitiveness scores, GDP per capita, and HDI score was conducted. Correlation analysis showed that NHRD competitiveness is highly correlated with national competitiveness, level of human development, and level of national economy.
The results show that Switzerland ranked first among 34 OECD countries in terms of the competitive advantage of its NHRD system. All of the Northern European countries—Sweden (2nd), Finland (5th), Denmark (6th), Norway (7th), and Iceland (9th)—are ranked among the top 10 countries. Among non-European countries, USA (3rd), Canada (8th), and Australia (11th) ranked in the upper 1/3 of the group among 34 countries. East Asian countries’ rankings, such as those of Japan (22nd) and South Korea (23rd), were in the mid-level group. Countries of Eastern and Southern Europe—Poland (26th), Hungary (29th), Greece (31st), and Italy (28th), to name a few—and Central and South America—Chile (32nd) and Mexico (33rd)—ranked in the lower 1/3 group.
Despite a slight difference, rankings assessed from the GHRD index showed similarity with the result of the GTI. In the GTI index, 4 Northern European countries—Sweden (7th), Finland (3rd), Denmark (2nd), and Norway (4th)—are ranked among the top 10 countries together with the United States (1st), Australia (6th), Singapore (5th), Hong Kong (8th), and Switzerland (9th). East Asian countries such as South Korea, Japan, and China ranked 22nd, 27th, and 33rd, respectively. Furthermore, in the forecasted ranking of talent competitiveness for 2015, the top 10 countries present a similar result. One thing different is that Israel enters the top 10 in the GTI in spite of ranking around 20th in the GHRD index and TGCI. On the other hand, in TGCI, the rankings of East Asian countries such as those of Japan (5th) and China (25th) are higher than in other indices although the result of Northern European countries, the United States, Singapore, and Switzerland ranking in the top 10 is identical.
In both assessments of the GHRD index and GTI, the fact that such Northern European countries as Finland, Denmark, Sweden, and Norway ranked high was noticeable. Nevertheless, the strength of the GHRD index is that it is based on more solid theoretical background and previous empirical evidences in its individual indicators, so suggestions for improvement in weak areas are more likely to be reliable and workable at the national level. The GHRD index developed and validated in this study can be used to assess and evaluate the current status of the NHRD system of a country and to compare each country’s level with that of other countries.
The purpose of assessing and evaluating the level and competitiveness of each country’s NHRD system is to suggest the direction for increasing the competitive advantage of human resources at a national level and, furthermore, to support policy development by analyzing the strengths and weaknesses of each country’s NHRD system. Therefore, the focus must not only be on ranking countries or creating a hierarchy among the countries using the index without considering the historical and environmental context in which each country’s NHRD system has progressed. This index would be more valuable when it is instead used as objective criteria for setting policy priorities or for benchmarking information for improvement.
It is in this regard that NHRD is also defined as Neo-Developmental Education (Lee 2009, p. 359).
Harbison-Myers index is the sum of secondary enrollment and tertiary enrollment times five (more weight was placed at tertiary education)
The detailed information on these experts was described in the Methods section of this paper.
With the GHRD Index, the NHRD competitiveness score of a country was calculated as follows: First, the data were collected from databases such as OECD Factbook 2010, OECD Education at a Glance 2010, ILO Labor Statistics Database, WEF Global Competitiveness Report 2009-2010/2010-2011, UNESCO Database. Missing values were replaced with estimated ones produced through the multiple imputation method, and the data were normalized to have an identical range of 1 to 7. Then, the normalized data of each indicator were multiplied by the weight produced by the AHP. The multiplied values of the 45 indicators were summed up to obtain the NHRD competitiveness score of a country.
This research was supported by the National Research Foundation of Korea Grant funded by the Korean Government (NRF 700-20110142). This research modified and validated the conceptual model and indicators of NHRD competitiveness index developed by Oh, et al. (2011). Authors thank to reviewers for invaluable comments for this article.
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